Papers with LENS
LENS: Learning Entities from Narratives of Skin Cancer (2025.coling-demos)
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| Challenge: | Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related forums. |
| Approach: | They propose to use reddit forums to create an automatic entity recognition system that can be used to predict skin cancer outcomes. |
| Outcome: | LENS achieves an overall entity-level F1 score of 0.561 . other notable results include “CANC_T” (0.747), “STG” (0.888), “POB” (0.914), “GENDER” (0.750), “A/G” (00.646), “EMO” (0.619), and “MHD” (0.503). |
DETECT: Determining Ease and Textual Clarity of German Text Simplifications (2026.eacl-long)
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| Challenge: | Current evaluation of German automatic text simplification relies on general-purpose metrics such as SARI, BLEU, and BERTScore. |
| Approach: | They propose a German-specific metric that holistically evaluates ATS quality across all three dimensions of simplicity, meaning preservation, and fluency. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than widely used ATS metrics. |
Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
Finding Support Examples for In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning is a new learning paradigm where a language model observes a few examples and directly outputs the test input’s prediction. |
| Approach: | They propose a method to find “support examples” for in-context learning by filtering a training dataset and a progressive filtering process to filter out uninformative examples. |
| Outcome: | The proposed method outperforms baselines and shows that each component contributes critically to the improvements. |
Less Noise, More Voice: Reinforcement Learning for Reasoning via Instruction Purification (2026.findings-acl)
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| Challenge: | Experimental results show that LENS outperforms GRPO in delivering higher performance and faster convergence. |
| Approach: | They propose a framework that purifies prompts by identifying and removing interference tokens and then transfers successful rollouts to supervise policy optimization on original noisy prompts. |
| Outcome: | The proposed framework outperforms GRPO in the real-world, with a 3.88% gain and speedup. |
LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models (2026.acl-long)
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Wenxuan Xu, Arvind Pillai, Subigya Nepal, Amanda C. Collins, Daniel M Mackin, Michael V. Heinz, Tess Z Griffin, Nicholas C. Jacobson, Andrew Campbell
| Challenge: | Current LLMs cannot natively ingest long-duration sensor streams and paired sensor–text datasets are scarce. |
| Approach: | They propose a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. |
| Outcome: | The proposed framework outperforms baselines on NLP metrics and task-specific measures of symptom severity and clinically meaningful narratives. |
LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)
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| Challenge: | Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach. |
| Approach: | They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner . |
| Outcome: | The proposed model correlates much better with human judgment than existing metrics. |
Enhancing Lexicon-Based Text Embeddings with Large Language Models (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. |
| Approach: | They introduce the first lexicon-based embeddings that consolidates the vocabulary space through token embeddation clustering to handle the issue of token redundancy in LLM vocabularies. |
| Outcome: | The proposed model outperforms dense embeddings on the Massive Text Embedding Benchmark (MTEB) it also supports efficient dimension pruning without any specialized objectives like Matryoshka Representation Learning. |
Mixtures of In-Context Learners (2025.acl-long)
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| Challenge: | In-context learning is sensitive to the choice of in-con context demonstrations and processing many demonstrations can be computationally demanding. |
| Approach: | They propose a method that uses subsets of demonstrations to train experts via ICL and learns a weighting function to merge their output distributions via gradient-based optimisation. |
| Outcome: | The proposed approach improves on 5 out of 7 classification datasets compared to strong baselines and reduces the inference time needed to achieve the same performance with fewer demonstrations. |
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance (2026.acl-long)
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| Challenge: | a large-scale evaluation of deployed LLMs under natural prompt distribution shift is needed . natural prompt behavior shifts can cause performance degradation in dynamic, real-world settings . |
| Approach: | They propose a data-centric framework for measuring natural prompt distribution shift . they train models on 4.68M training prompts and evaluate on 57.6k prompts . |
| Outcome: | The proposed framework evaluates natural prompt distribution shift in LLMs over time and between user groups. |